{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.models import load_model\n",
    "from keras.utils import to_categorical\n",
    "from keras.layers.core import Dropout\n",
    "\n",
    "import time\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "from keras import backend as K\n",
    "# import icecream.ic as ic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = pd.read_csv('X_train.csv')\n",
    "Y_train = pd.read_csv('Y_train.csv')\n",
    "X_test = pd.read_csv('X_test.csv')\n",
    "Y_test = pd.read_csv('Y_test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0     -1.061735\n",
      "1      0.462556\n",
      "2     -0.526620\n",
      "3      0.605477\n",
      "4     -0.501667\n",
      "        ...    \n",
      "95    10.013376\n",
      "96    10.065144\n",
      "97     9.458488\n",
      "98     9.898357\n",
      "99    12.071141\n",
      "Name: x1, Length: 100, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# print(X_train['x1'])\n",
    "print(X_train.iloc[:, 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2be28f573d0>]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.plot(X_train['x1'], X_train['x2'], '.')\n",
    "plt.plot(X_train.iloc[:, 0], X_train.iloc[:, 1], '.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    }
   ],
   "source": [
    "print(type(X_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "print(type(X_train.values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
